if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
print('Using config:')
pprint.pprint(cfg)
# while not os.path.exists(args.model) and args.wait:
# print('Waiting for {} to exist...'.format(args.model))
# time.sleep(1000)
weights_filename = os.path.splitext(os.path.basename(args.model))[0]
imdb = get_imdb(args.imdb_name)
imdb.competition_mode(args.comp_mode)
device_name = '/gpu:{:d}'.format(args.gpu_id)
print device_name
network = get_network(args.network_name)
print 'Use network `{:s}` in training'.format(args.network_name)
cfg.GPU_ID = args.gpu_id
# start a session
saver = tf.train.Saver()
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
saver.restore(sess, args.model)
print('Loading model weights from {:s}').format(args.model)
test_net(sess, network, imdb, weights_filename)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
print('Using config:')
pprint.pprint(cfg)
while not os.path.exists(args.model) and args.wait:
print('Waiting for {} to exist...'.format(args.model))
time.sleep(10)
weights_filename = os.path.splitext(os.path.basename(args.model))[0]
imdb = get_imdb(args.imdb_name)
imdb.competition_mode(args.comp_mode)
device_name = '/gpu:{:d}'.format(args.gpu_id)
print device_name
network = get_network(args.network_name)
print 'Use network `{:s}` in training'.format(args.network_name)
cfg.GPU_ID = args.gpu_id
# start a session
saver = tf.train.Saver()
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
saver.restore(sess, args.model)
print('Loading model weights from {:s}').format(args.model)
test_net(sess, network, imdb, weights_filename, vis=args.vis)
dest='imdb_name',
choices=['caltech_test', 'caltech_train'],
default='caltech_test',
type=str)
parser.add_argument('--network',
dest='network_name',
choices=['VGGnet_test', 'MSnet_test'],
default='VGGnet_test')
parser.add_argument('--vis', action='store_true')
if len(sys.argv) == 1:
parser.print_help()
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
pprint.pprint(cfg)
weights_filename = os.path.splitext(os.path.basename(args.model))[0]
imdb = get_imdb(args.imdb_name)
print 'Use device /gpu:0'
network = get_network(args.network_name)
print 'Use network `{:s}` in training'.format(args.network_name)
saver = tf.train.Saver()
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
saver.restore(sess, args.model)
print('Loading model weights from {:s}').format(args.model)
test_net(sess, network, imdb, weights_filename, vis=args.vis, thresh=0.05)
while not os.path.exists(args.model) and args.wait:
print(('Waiting for {} to exist...'.format(args.model)))
time.sleep(1000)
weights_filename = os.path.splitext(os.path.basename(args.model))[0]
imdb = get_imdb(args.imdb_name)
imdb.competition_mode(args.comp_mode)
device_name = '/gpu:{:d}'.format(args.gpu_id)
print(device_name)
with tf.device(device_name):
network = get_network(args.network_name)
print(('Use network `{:s}` in training'.format(args.network_name)))
cfg.GPU_ID = args.gpu_id
# import os
# os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # see issue #152
# os.environ["CUDA_VISIBLE_DEVICES"]=str(args.gpu_id)
# start a session
saver = tf.train.Saver()
c = tf.ConfigProto(allow_soft_placement=True)
c.gpu_options.visible_device_list = str(args.gpu_id)
sess = tf.Session(config=c)
saver.restore(sess, tf.train.latest_checkpoint(args.model))
print((('Loading model weights from {:s}').format(args.model)))
test_net(sess, network, imdb, weights_filename, thresh=0.7)
# load_test_net(sess, network, imdb, weights_filename)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
print('Using config:')
pprint.pprint(cfg)
while not os.path.exists(args.model + '.index') and args.wait:
print('Waiting for {} to exist...'.format(args.model))
time.sleep(1000)
weights_filename = os.path.splitext(os.path.basename(args.model))[0]
imdb = get_imdb(args.imdb_name)
imdb.competition_mode(args.comp_mode)
device_name = '/gpu:{:d}'.format(args.gpu_id)
print device_name
network = get_network(args.network_name)
print 'Use network `{:s}` in training'.format(args.network_name)
cfg.GPU_ID = args.gpu_id
# start a session
saver = tf.train.Saver()
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
saver.restore(sess, args.model)
print ('Loading model weights from {:s}').format(args.model)
test_net(sess, network, imdb, weights_filename, output_dir=args.output_dir)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
print('Using config:')
pprint.pprint(cfg)
while not os.path.exists(args.model) and args.wait:
print('Waiting for {} to exist...'.format(args.model))
time.sleep(1000)
weights_filename = os.path.splitext(os.path.basename(args.model))[0]
imdb = get_imdb(args.imdb_name)
imdb.competition_mode(args.comp_mode)
device_name = '/gpu:{:d}'.format(args.gpu_id)
print device_name
network = get_network(args.network_name)
print 'Use network `{:s}` in training'.format(args.network_name)
cfg.GPU_ID = args.gpu_id
# start a session
saver = tf.train.Saver()
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
saver.restore(sess, args.model)
print ('Loading model weights from {:s}').format(args.model)
test_net(sess, network, imdb, weights_filename)
